• phone icon +44 7459 302492 email message icon support@uplatz.com
  • Register

BUY THIS COURSE (GBP 12 GBP 29)
4.9 (2 reviews)
( 10 Students )
 

IoT Edge Computing

Learn how to design, deploy, and manage IoT edge computing systems that process data locally, reduce latency, improve reliability, and power real-time
( add to cart )
ALL COURSES Course URL
Save 59% Offer ends on 31-Dec-2026
Course Duration: 10 Hours
  Price Match Guarantee   Full Lifetime Access     Access on any Device   Technical Support    Secure Checkout   Course Completion Certificate
New & Hot
Trending
Popular
Coming soon (2026)

Students also bought -

Completed the course? Request here for Certificate. ALL COURSES

The rapid expansion of the Internet of Things (IoT) has led to billions of connected devices generating massive volumes of data every second. Sensors, cameras, industrial machines, vehicles, medical devices, and smart infrastructure continuously produce streams of information that must be processed, analyzed, and acted upon in real time. While cloud computing has traditionally handled large-scale data processing, relying solely on the cloud is no longer sufficient for many IoT use cases. High latency, limited bandwidth, privacy concerns, intermittent connectivity, and rising cloud costs have driven the shift toward IoT Edge Computing.
 
IoT Edge Computing brings computation, storage, and intelligence closer to where data is generated — at or near the physical devices themselves. By processing data locally at the edge, systems can respond instantly, operate reliably even when connectivity is limited, and reduce the volume of data sent to the cloud. Edge computing has become a foundational technology for modern digital systems, enabling real-time analytics, autonomous decision-making, and AI-driven applications across industries.
 
The IoT Edge Computing course by Uplatz provides a comprehensive, practical introduction to designing, building, and deploying intelligent edge systems. This course covers the full lifecycle of edge computing — from IoT device architecture and communication protocols to edge analytics, AI at the edge, orchestration, security, and integration with cloud platforms. Learners will gain a clear understanding of how edge computing complements cloud and fog architectures, and how to choose the right deployment model for different use cases.
🔍 What Is IoT Edge Computing?
 
IoT Edge Computing is a distributed computing paradigm in which data processing and analytics are performed close to the source of data generation rather than in centralized cloud data centers. Edge nodes can include:

  • IoT gateways

  • Embedded devices

  • Industrial controllers

  • Smart cameras

  • Edge servers

  • On-device AI accelerators

By processing data locally, edge systems can make decisions in milliseconds, enabling real-time control and automation.
 
Key characteristics of IoT edge computing include:

  • Low latency and real-time responsiveness

  • Reduced bandwidth usage

  • Improved reliability and offline operation

  • Enhanced data privacy and security

  • Scalability across millions of devices

Edge computing does not replace the cloud; instead, it works alongside cloud systems, forming a cloud–edge–device continuum.
⚙️ How IoT Edge Computing Works
 
1. Data Generation at the Device Layer
 
Sensors and devices generate raw data such as temperature readings, video streams, vibration signals, or telemetry.
 
2. Edge Processing Layer
 
Edge devices or gateways perform:

  • Data filtering and aggregation

  • Event detection

  • Real-time analytics

  • AI inference using trained models

3. Edge Intelligence (AI at the Edge)
 
Machine learning models run locally to:

  • Detect anomalies

  • Classify images or audio

  • Predict failures

  • Trigger automated actions

4. Communication & Messaging
 
Protocols commonly used include:

  • MQTT

  • CoAP

  • AMQP

  • HTTP/REST

5. Cloud Integration
 
Only relevant or summarized data is sent to the cloud for:

  • Long-term storage

  • Model training

  • Fleet management

  • Monitoring and dashboards

6. Orchestration & Management
 
Edge workloads are managed using:

  • Containers (Docker)

  • Kubernetes at the edge (K3s, MicroK8s)

  • OTA updates

  • Remote monitoring

This layered approach enables scalable, intelligent IoT systems.
🏭 Where IoT Edge Computing Is Used in the Industry
 
1. Manufacturing & Industry 4.0
 
Real-time monitoring, predictive maintenance, robotics, and automation.
 
2. Smart Cities
 
Traffic control, surveillance, waste management, and energy optimization.
 
3. Healthcare
 
Remote patient monitoring, medical imaging analysis, wearable devices.
 
4. Autonomous Vehicles & Transportation
 
Real-time perception, navigation, and safety systems.
 
5. Retail
 
Smart shelves, customer analytics, inventory tracking.
 
6. Energy & Utilities
 
Smart grids, fault detection, renewable energy optimization.
 
7. Agriculture
 
Precision farming, soil monitoring, automated irrigation.
 
Edge computing enables these systems to operate with speed, resilience, and intelligence.
🌟 Benefits of Learning IoT Edge Computing
 
By mastering IoT edge computing, learners gain:

  • Ability to design low-latency, real-time systems

  • Skills to reduce cloud dependency and cost

  • Knowledge of AI deployment on constrained devices

  • Understanding of edge security and privacy

  • Experience with modern IoT architectures

  • High-demand skills across multiple industries

Edge computing expertise is increasingly essential as IoT adoption accelerates globally.
📘 What You’ll Learn in This Course
 
You will explore:

  • IoT and edge computing fundamentals

  • Device, gateway, and edge architectures

  • IoT communication protocols

  • Edge analytics and stream processing

  • AI and ML at the edge

  • Containerization and orchestration at the edge

  • Security, identity, and device management

  • Cloud–edge integration patterns

  • Real-world IoT edge use cases

  • Capstone: build an end-to-end IoT edge system

🧠 How to Use This Course Effectively

  • Start with IoT and networking basics

  • Understand edge vs cloud responsibilities

  • Build simple edge processing pipelines

  • Deploy lightweight ML models at the edge

  • Experiment with containerized edge workloads

  • Integrate with cloud dashboards

  • Complete the capstone project

👩‍💻 Who Should Take This Course

  • IoT Engineers

  • Embedded Systems Developers

  • Cloud & Edge Architects

  • AI/ML Engineers

  • DevOps Engineers

  • Robotics Engineers

  • Students entering IoT or edge computing fields

Basic programming knowledge (Python/C/C++) is helpful.
🚀 Final Takeaway
 
IoT Edge Computing is a cornerstone of modern intelligent systems, enabling real-time decision-making, efficiency, and scalability across connected environments. By mastering edge computing, you gain the ability to build robust IoT solutions that operate reliably, securely, and intelligently in the real world.

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand IoT and edge computing architectures

  • Design edge-based data processing pipelines

  • Deploy AI models at the edge

  • Use IoT communication protocols effectively

  • Integrate edge systems with the cloud

  • Implement security and device management

  • Build a complete IoT edge computing solution

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to IoT & Edge Computing

  • IoT evolution

  • Edge vs cloud vs fog

Module 2: IoT Devices & Sensors

  • Hardware overview

  • Data generation

Module 3: Edge Architecture & Gateways

  • Edge nodes

  • Processing layers

Module 4: Communication Protocols

  • MQTT, CoAP, HTTP

Module 5: Edge Analytics

  • Filtering, aggregation

  • Stream processing

Module 6: AI at the Edge

  • Model deployment

  • Inference optimization

Module 7: Containers & Orchestration

  • Docker

  • Kubernetes at the edge

Module 8: Security & Device Management

  • Identity

  • Encryption

  • OTA updates

Module 9: Cloud Integration

  • AWS IoT

  • Azure IoT

  • Google Cloud IoT

Module 10: Capstone Project

  • End-to-end IoT edge system

Certification Back to Top

Learners receive a Uplatz Certificate in IoT Edge Computing, validating expertise in designing and deploying intelligent edge-based IoT systems.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • IoT Engineer

  • Edge Computing Engineer

  • Embedded Systems Engineer

  • Cloud & Edge Architect

  • AI Engineer (Edge AI)

  • DevOps Engineer (IoT)

  • Robotics & Automation Engineer

Interview Questions Back to Top

1. What is IoT edge computing?

Processing data near the source instead of sending everything to the cloud.

2. Why is edge computing needed?

To reduce latency, bandwidth usage, and improve reliability.

3. How does edge computing differ from cloud computing?

Edge processes data locally; cloud handles centralized storage and analytics.

4. What are common edge devices?

IoT gateways, embedded boards, smart cameras, edge servers.

5. Which protocols are used in IoT?

MQTT, CoAP, HTTP, AMQP.

6. What is AI at the edge?

Running machine learning inference on edge devices.

7. How is security handled at the edge?

Through encryption, device identity, access control, and OTA updates.

8. What is an IoT gateway?

A bridge between devices and cloud systems that processes data locally.

9. Can edge systems work offline?

Yes, edge systems can operate independently when connectivity is limited.

10. What industries use edge computing?

Manufacturing, healthcare, transportation, energy, retail, and smart cities.

Course Quiz Back to Top
Start Quiz
Back to Top



BUY THIS COURSE (GBP 12 GBP 29)